Inset-fed microstrip patch antenna optimization for 2.4 GHz using surrogate model assisted differential evolution machine learning algorithm
Bibliographic record
Abstract
In this work, we have used the surrogate model assisted differential evolution (SADEA) to model a one and two-element inset-fed patch antenna array to optimize its parameters for efficiency and usability. The microstrip patch antennas operates in a frequency band of 2.4 GHz. The optimization process focused on fine-tuning the patch length, patch width, and notch width to enhance key performance metrics directivity, return loss, and bandwidth. The design is made in CST software with an FR-4 substrate and simulated in the ADE1.0 software a MATLAB toolbox. Significant enhancements were achieved including a directivity gain of 3.04 dB, and 5.58 dB a return loss of -19 dB, -16 dB, and an expanded impedance bandwidth from 0.0798 GHz, 0.0588 GHz to 0.0951 GHz, 0.0824 GHz respectively. The antenna was constructed and then measured. The findings showed that the measurements and the fabrication process closely matched, especially in terms of return loss.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".